The Benefits of Wearable AI in Post-Discharge Management of AMI Patients

The Benefits of Wearable Device-Based Artificial Intelligence in Post-Discharge Management of Patients With Acute Myocardial Infarction

Myocardial infarction (MI) remains a major threat to human health. Although interventional treatment techniques have advanced rapidly, many patients still experience major adverse cardiovascular events (MACE) and require hospital readmission after discharge. Artificial intelligence (AI) based on wearable device data has shown great potential in the diagnosis and management of cardiovascular diseases.

This study aims to explore the clinical value of wearable device-based data analysis and AI-driven risk stratification models in post-discharge management of acute myocardial infarction (AMI) patients.

Study Overview

• Status: Not yet recruiting
• Conditions: Heart Failure; Acute Myocardial Infarction
• Intervention / Treatment: Combination product: Optimized Integrated Management Based on AI-Guided Wearable Data

Detailed Description

This prospective, open-label, randomized controlled study aims to evaluate the clinical benefits of wearable device-based AI risk models in post-discharge management of AMI patients. A total of 200 patients who have undergone PCI and provided informed consent will be enrolled, including those with both preserved and reduced left ventricular ejection fraction (LVEF).

Participants will be randomly assigned to either the control group or the intervention group in a 1:1 ratio. All patients will be equipped with a wearable smartwatch and continuously monitored for 3 months after discharge. Data collected will include physiological signals, sleep and activity parameters. In both groups, patients will receive weekly telephone follow-ups and monthly office visits to record symptoms, medication use, and adverse events.

In the intervention group, wearable data and AI analytical results will be made available to both patients and their physicians. These insights will be discussed during follow-ups and used to support lifestyle modification, medication adjustment, and clinical decision-making. In the control group, AI data will be collected but not shared or used for clinical management during the study period.

The primary study endpoint is the time to first unplanned hospital readmission within 3 months, including readmissions due to chest pain, heart failure, arrhythmia, recurrent myocardial infarction, or death. The secondary endpoints include: Change in Kansas City Cardiomyopathy Questionnaire-12 (KCCQ-12) score from baseline to 3 months; change in left ventricular ejection fraction (LVEF) measured by echocardiography between baseline and 3 months.

The investigators hypothesize that AI-assisted, wearable-based monitoring and feedback will improve early detection of adverse cardiovascular events, reduce unplanned hospitalizations, increase LVEF in patients with reduced LVEF at discharge, and enhance quality of life compared with standard post-discharge care.

• Study Type: Interventional
• Enrollment (Estimated): 200
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: ZHIGUO ZOU, MD, PhD; Phone Number: +86 13524596108; Email: zouzhiguo@renji.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• Adults aged 18 to 75 years.
• Confirmed diagnosis of acute myocardial infarction (AMI), including both ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI).
• Underwent successful percutaneous coronary intervention (PCI) during index hospitalization.
• Hemodynamically stable at the time of hospital discharge.
• Willing and able to wear a smartwatch continuously for the study period.
• Compatible with the data collection application and have stable internet access.

Exclusion Criteria:

• Planned staged or elective PCI or any coronary revascularization scheduled within 3 months after discharge.
• Unable to tolerate or contraindicated for wearing metal or electronic monitoring devices.
• Pregnant or breastfeeding women.
• Residence in an area without stable network connectivity or inability to use a smartphone for data upload and communication.
• Severe comorbidities that limit 3-month survival or follow-up.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Treatment
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: None (Open Label)

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
No Intervention: The guideline-guided traditional management group; As the control group, wearable data will be collected but not shared with the participant and responding physician or used for clinical management during the study period. All management in the participants is based on updated clinical guidelines.
Experimental: The guideline-guided and wearable-assisted management group; As the intervention group, in addition to clinical guidelines, wearable data and AI analytical results will be made available to both patients and their physicians. These insights will be discussed during follow-ups and used to support lifestyle modification, medication adjustment, and clinical decision-making.	Combination product: Optimized Integrated Management Based on AI-Guided Wearable Data; The collected data will be shared with both patients and their treating physicians during follow-up visits. Based on these insights, the clinical team will offer personalized recommendations regarding medication adjustment, lifestyle modification, diet optimization, and physical activity guidance.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Time to First Unplanned Re-hospitalization event	The primary study endpoint is the time to first unplanned hospital readmission within 3 months, including readmissions due to chest pain, heart failure, arrhythmia, recurrent myocardial infarction, or death.	From the date of hospital discharge to 3 months post-discharge (90 days).

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Change in LVEF	LVEF will be assessed by transthoracic echocardiography at discharge (baseline) and at 3 months post-discharge follow-up. The change in LVEF will be calculated as the difference between the two measurements.	At baseline and at 3 months post-discharge
Change in the score of Kansas City Cardiomyopathy Questionnaire-12	The KCCQ-12, a validated patient-reported outcome measure, will be administered during the index hospitalization (prior to discharge) and again at 3 months post-discharge follow-up.	At baseline and at 3 months post-discharge.

Collaborators and Investigators

• Sponsor: RenJi Hospital

Study record dates

Study Major Dates

• Study Start (Estimated): December 30, 2025
• Primary Completion (Estimated): June 30, 2026
• Study Completion (Estimated): December 30, 2026

Study Registration Dates

• First Submitted: November 14, 2025
• First Submitted That Met QC Criteria: December 15, 2025
• First Posted (Actual): December 17, 2025

Study Record Updates

• Last Update Posted (Actual): December 17, 2025
• Last Update Submitted That Met QC Criteria: December 15, 2025
• Last Verified: December 1, 2025

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Cardiovascular Diseases; Heart Diseases; Heart Failure
• Other Study ID Numbers: EARLY-MYO Wearable AI

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No